scholarly journals Efficiency of a "Small-World" Brain Network Depends on Consciousness Level: A Resting-State fMRI Study

2013 ◽  
Vol 24 (6) ◽  
pp. 1529-1539 ◽  
Author(s):  
T. Uehara ◽  
T. Yamasaki ◽  
T. Okamoto ◽  
T. Koike ◽  
S. Kan ◽  
...  
Keyword(s):  
Resting State ◽  
Small World ◽  
Resting State Fmri ◽  
Brain Network ◽  
Consciousness Level ◽  
Fmri Study ◽  
World Brain

scholarly journals Disrupted Small-World Brain Networks in Moderate Alzheimer's Disease: A Resting-State fMRI Study

PLoS ONE ◽  
2012 ◽  
Vol 7 (3) ◽  
pp. e33540 ◽  
Author(s):  
Xiaohu Zhao ◽  
Yong Liu ◽  
Xiangbin Wang ◽  
Bing Liu ◽  
Qian Xi ◽  
...  
Keyword(s):  
Alzheimer’S Disease ◽  
Alzheimer's Disease ◽  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Resting State Fmri ◽  
Fmri Study ◽  
World Brain

scholarly journals Parcellation-dependent small-world brain functional networks: A resting-state fMRI study

2009 ◽  
Vol 30 (5) ◽  
pp. 1511-1523 ◽  
Author(s):  
Jinhui Wang ◽  
Liang Wang ◽  
Yufeng Zang ◽  
Hong Yang ◽  
Hehan Tang ◽  
...  
Keyword(s):  
Resting State ◽  
Small World ◽  
Resting State Fmri ◽  
Functional Networks ◽  
Brain Functional Networks ◽  
Fmri Study ◽  
World Brain

scholarly journals Disrupted brain network topology in pediatric posttraumatic stress disorder: A resting-state fMRI study

2015 ◽  
Vol 36 (9) ◽  
pp. 3677-3686 ◽  
Author(s):  
Xueling Suo ◽  
Du Lei ◽  
Kaiming Li ◽  
Fuqin Chen ◽  
Fei Li ◽  
...  
Keyword(s):  
Posttraumatic Stress Disorder ◽  
Posttraumatic Stress ◽  
Network Topology ◽  
Resting State ◽  
Stress Disorder ◽  
Resting State Fmri ◽  
Brain Network ◽  
Fmri Study

Schizophrenia symptoms and brain network efficiency: A resting-state fMRI study

2015 ◽  
Vol 234 (2) ◽  
pp. 208-218 ◽  
Author(s):  
Tsung-Wei Su ◽  
Tun-Wei Hsu ◽  
Yi-Ching Lin ◽  
Ching-Po Lin
Keyword(s):  
Resting State ◽  
Resting State Fmri ◽  
Brain Network ◽  
Network Efficiency ◽  
Fmri Study

scholarly journals Efficient small-world and scale-free functional brain networks at rest using k-nearest neighbors thresholding

2019 ◽  
Author(s):  
Caroline Garcia Forlim ◽  
Siavash Haghiri ◽  
Sandra Düzel ◽  
Simone Kühn
Keyword(s):  
Resting State ◽  
Brain Networks ◽  
Small World ◽  
Nearest Neighbors ◽  
Resting State Fmri ◽  
Brain Network ◽  
K Nearest Neighbors ◽  
Scale Free ◽  
The Brain ◽  
Density Threshold

AbstractIn recent years, there has been a massive effort to analyze the topological properties of brain networks. Yet, one of the challenging questions in the field is how to construct brain networks based on the connectivity values derived from neuroimaging methods. From a theoretical point of view, it is plausible that the brain would have evolved to minimize energetic costs of information processing, and therefore, maximizes efficiency as well as to redirect its function in an adaptive fashion, that is, resilience. A brain network with such features, when characterized using graph analysis, would present small-world and scale-free properties.In this paper, we focused on how the brain network is constructed by introducing and testing an alternative method: k-nearest neighbor (kNN). In addition, we compared the kNN method with one of the most common methods in neuroscience: namely the density threshold. We performed our analyses on functional connectivity matrices derived from resting state fMRI of a big imaging cohort (N=434) of young and older healthy participants. The topology of networks was characterized by the graph measures degree, characteristic path length, clustering coefficient and small world. In addition, we verified whether kNN produces scale-free networks. We showed that networks built by kNN presented advantages over traditional thresholding methods, namely greater values for small-world (linked to efficiency of networks) than those derived by means of density thresholds and moreover, it presented also scale-free properties (linked to the resilience of networks), where density threshold did not. A brain network with such properties would have advantages in terms of efficiency, rapid adaptive reconfiguration and resilience, features of brain networks that are relevant for plasticity and cognition as well as neurological diseases as stroke and dementia.HighlightsA novel thresholding method for brain networks based on k-nearest neighbors (kNN)kNN applied on resting state fMRI from a big cohort of healthy subjects BASE-IIkNN built networks present greater small world properties than density thresholdkNN built networks present scale-free properties whereas density threshold did not

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